(1) Field of the Invention
The present invention relates to an eye-gaze tracking device, an eye-gaze tracking method, an electro-oculography measuring device, a wearable camera, a head-mounted display, electronic eyeglasses, and an ophthalmological diagnosis device.
(2) Description of the Related Art
Conventionally, an eye-gaze tracking technique using Electro-oculography (EOG) is well known. The technique is to detect an eye-gaze by measuring eye potential (electro-oculogram) generated by a positive charge in a cornea and a negative charge in a retina, using a plurality of electrodes attached around eyes. Unlike an eye-gaze tracking technique for capturing an image of an eyeball using a camera, this technique using EOG has such advantages as not interfering with vision, not being influenced by outside light, not depending on a shape and an opening state of the eye, and achieving low power consumption, and thus is expected to be applied to various devices.
However, as shown in a waveform example (an example of three electrodes) in FIG. 1, since a low frequency noise (scores of mV) that is 100 or more times the electro-oculogram (hundreds of uV, an extended portion in FIG. 1) is mixed into the observation voltage, and an electro-oculography base line (DC) fluctuates (drifts), the observation voltage exceeds an electro-oculography range within approximately one minute, thus disabling gaze detection. In addition, the frequency range overlaps with the electro-oculogram, and it is not possible to perform frequency separation.
There are two major methods for correcting a drift as below.
<Conventional Method (1): Eye-Gaze Estimation Using an Eyeball Battery Model>
First, there is an eye-gaze tracking method (Patent Reference 1 and Non-Patent Reference 2) using a model which resembles a battery as an eyeball (Non-Patent Reference 1). Conventionally, the relationship between an eye gaze and EOG has been linearly approximated, but accuracy in gaze detection has been low due to the fact that a larger gaze angle results in greater nonlinearity. Thus, as an FOG nonlinear model, Non-Patent Reference 1 suggests a model (battery model), which assumes a cornea of an eyeball as a plus battery and a retina as a minus battery, and assumes eyeball movement as a rotation of batteries. When r and r′ represent distances from the respective electrodes to a cornea center and a retina center, I is a current flowing from the retina to cornea within the eyeball, and δ is conductivity around the eyeballs, potential v generated at the electrode is calculated in accordance with (Expression 1) below:
                    [                  Math          .                                          ⁢          1                ]                                                            v        =                              I                          4              ⁢                                                          ⁢              πσ                                ⁢                      (                                          1                r                            -                              1                                  r                  ′                                                      )                                              (                  Expression          ⁢                                          ⁢          1                )            
Patent Reference 1 and Non-Patent Reference 2 assume that a drift is caused by a temporal fluctuation of the current I, and also estimate, by EM algorithm, a gaze position and the current I such that a least square error between the observation voltage and a theoretical voltage calculated using the battery model is smallest.
<Conventional Method (2): Eye-Gaze Estimation Using Kalman Filter>
For another conventional method, there is a method using Kalman filter (Non-Patent References 3 and 4). An EOG(t) measured from a pair of electrodes is modeled as shown in (Expression 2A) and (Expression 2B) below, using: a two-dimensional vector x(t) which represents a gaze direction; a conversion matrix Z for converting the gaze direction into EOG; and a noise component e(t) including a DC offset and a drift:
[Math 2]EOG(t)=Z·x(t)+e(t)  (Expression 2A)ΔEOG(t)=Z·Δx(t)+Δe(t)  (Observation equation) (Expression 2B)
In addition, movement of the gaze is modeled using a state transition matrix F(t) and a state estimation error w(t), as shown in (Expression 3) below, and is predicted as shown by:
[Math 3]Δx(t+1)=F(t)·Δx(t)+w(t)  (State equation) (Expression 3)
By solving these observation equation and state equation by Kalman filter, the gaze direction x(t) is estimated.
In addition, it is possible to respond to both problems of variation in drift amount that varies between each electrode, and of signal abnormality occurring at a particular electrode (due to falling of the electrode or change in contact state) by appropriately applying a covariance matrix Δe(t); thus, a robust eye-gaze estimation is performed by assuming, as noise, the signal generated by subtracting the EOG component involved in eyeball movement from the observation value, and updating the covariance matrix Δe(t).
[Patent Reference]
[Patent Reference]
[Patent Reference 1] Japanese Unexamined Patent Application Publication 2007-252879
[Non-Patent Reference]
[Non-Patent Reference 1] Itsuki, et. Al “A Battery Model of the Eyeball to Calculate Standing Potential of the Eye”, Journal of Japanese Ophthalmological Society Vol. 99, No. 9, pp. 1012-1016, Sep. 10, 1995
[Non-Patent Reference 2] Mizoo, Advisor: Sakaguchi, “Eyeball Position Measuring System Based on Multipoint Electro-oculography”, the University of Electro-Communications masters thesis.
[Non-Patent Reference 3] Manabe, Fukumoto, “Full-time Wearable Headphone-type Gaze Detector” (in Japanese), Journal of Information Processing, Mar. 2, 2006, Vol. 2006, page 4, 23-24.
[Non-Patent Reference 4] H. Manabe, M. Fukumoto, “Full-time Wearable Headphone-type Gaze Detector”, CHI2006, Work-in-Progress, pp. 1073-1078.
However, the conventional technique (1) described above (battery model method) has considered that the drift is caused by amplitude fluctuation in EOG due to change in current I in (Expression 1). Evidently, EOG has characteristics that amplitude fluctuates due to change in amount of light incident on eyes (in ophthalmology, Arden ratio (EOG amplitude ratio between light and dark environment) is used as a test item), but a dominant cause of a drift is a baseline drift in EOG (DC variation) caused by biophysiological change, body motion, contact stability of the electrode, polarization at the electrode, and so on which occur even in an environment without light-dark fluctuations, and corresponds to the fluctuation in an offset term e(t) in (Expression 2) according to the conventional method (2). In other words, the conventional method (1) does not correct the drift of the baseline e(t) in EOG.
In addition, the conventional method (2) (Kalman filter method) does not describe the detail, but normally, Kalman filter assumes Gaussianity (normal distribution) of noise, and particularly is based on a premise that a noise distribution mean does not fluctuate. However, the drift, even when differentiated as shown in (Expression 3), is noise having a sharply-fluctuating mean value, and thus significantly deteriorating accuracy in estimating the gaze direction. That is, only predicting gaze movement is not sufficient, and gaze accuracy significantly deteriorates without estimation of the drift (especially, a mean value). In addition, although the relationship between the gaze and the electro-oculogram is linearly approximated using the conversion matrix Z, the closer the electrode is to the eyeball, the greater nonlinearity becomes, thus causing another problem of errors and deterioration in accuracy.